Expression of TM6SF2 in hepatocellular carcinoma tissue and its bioinformatics functions

Objective To investigate the expression of TM6 SF2 in hepatocellular carcinoma (HCC) tissue and its biological functions by data mining in tumor databases. Methods The GEPIA database was applied to measure the change in the mRNA expression level of TM6 SF2 in HCC tissue, and OncoLnc was used to analyze the association of TM6 SF2 expression with the survival time of HCC patients. The cBioPortal and LinkedOmics databases were used to analyze the genes associated with the expression of TM6 SF2 in HCC tissue, and the DAVID6. 8 and STRING databases were used to perform a bioinformatics analysis of TM6 SF2 and the genes associated with its expression. The t-test was used to investigate the difference in the mRNA expression of TM6 SF2 between HCC tissue and adjacent tissue. The Spearman correlation coefficient was used to analyze the correlation of gene expression. The Kaplan-Meier method was used to calculate survival percentage, and the log-rank test was used to analyze the difference in survival percentage. Results Compared with the normal liver tissue, the HCC tissue had low mRNA expression of TM6 SF2 (| log2 FC | cut-off = 0. 5, P < 0. 01) . Compared with those with high expression of TM6 SF2, the patients with low expression had a significant reduction in overall survival time (χ2= 9. 897, P < 0. 01) . Data analysis showed that a total of 49 genes were associated with the expression of TM6 SF2 in HCC tissue, and the gene ontology analysis showed that these genes were enriched in the biological processes and functions including fatty acid synthesis, fatty acid ligase activation, and thrombin regulation (P< 0. 05) . The Kyoto Encyclopedia of Genes and Genome pathway analysis showed that these genes were mainly involved in the signaling pathways of alanine metabolism, peroxisome proliferator-activated receptor signaling pathway, and bile secretion (P < 0. 05) . The protein-protein interaction network analysis showed that the genes of SERPINC1, NR1 I2, SERPINA10, and SLC10 A1 had marked or potential interaction with TM6 SF2 (P < 0. 01) . Conclusion Tumor data mining can quickly obtain the information on the expression of TM6 SF2 in HCC tissue and provide a bioinformatics basis for exploring the role of TM6 SF2 in the development and progression of HCC.